Tag Archives: toronto

Toronto publishes its candidates here http://app.toronto.ca/vote2010/findByOffice.do?officeType=2&officeName=Councillor in a kind of tabular format. All I want to do is count the number of candidates per ward, remembering that some wards have no candidates yet.

Being lazy, I’d far rather have another program parse the HTML, so I work from the formatted output of W3M. It’s relatively easy to munge the output using Perl. From there, I hope to stick the additional data either into a new column in the shapefile, or use SpatiaLite. I’m undecided.

As a start, here’s the Toronto Wards layer, rendered in QGIS with the ward number as a label:

You’ll notice that something is quite off. It looks like QGIS uses the centre of the minimum bounding rectangle of a polygon as the label point. While this is okay for nice regular shapes, weird glaikit shapes end up with the label outside the boundary. Not good.

I was about to give up on this completely, when I saw QGIS’s “Labeling” [sic] plugin. What it does is work out a variety of better visual positions for your labels. Here’s the setting I chose:

The result is much more pleasing:

Much better.

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For reasons that are not particularly clear, the Toronto.ca|Open data is in two different coordinate reference systems (CRS), MTM 3 Degree Zone 10, NAD27 (EPSG 2019) and UTM 6 Degree Zone 17N NAD27 (EPSG 26717). This confuses QGIS even if you’ve input the proper SRIDs into SpatiaLite. The image above shows two apparent Torontos, one in each of the CRSs.

What you have to do is go to to the Project Properties, select the Coordinate Reference System (CRS) tab, and “Enable ‘on the fly’ CRS transformation”. This will line those city layers right back up.

Once we do that, things align as they should. Here’s my neighbourhood, with its parks

The OGR Simple Feature Library is your friend. It can convert pretty much any geo format to another, and can transform coordinates between systems. In exchange for this power, it wants your soul is rather complex.

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Me and Catherine are quite partial to libraries. I’m going to use the address points database we made yesterday to find the libraries within 2km of a given address. It’s not a very useful query, but it shows the very basics of searching by distance.

I’m going to use the address from yesterday, 789 Yonge St. The fields I’m interested in are:

address – this is the street number (789)

lf_name – the street name, in all-caps, with the customary abbreviations for rd/ave/blvd, etc (YONGE ST)

fcode_desc – the type of the address. Most places don’t have this set, but here it’s ‘Library’.

geometry – the description of the feature’s locus. This isn’t human readable, but can be viewed with the AsText() function.

I’m also going to use a calculated field for the distance to make the query shorter. Since my map units are metres, calculating Distance(…)/1000 will return kilometres. So:

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I’m going to use SpatiaLite and the Toronto One Address Repository to try some simple geocoding. That is, given an address, spit out the real-world map coordinates. As it happens, the way the Toronto data is structured it doesn’t really need to use any GIS functions, just some SQL queries. There are faster and better ways to code this, but I’m just showing you how to load up data and run simple queries.

SpatiaLite is my definition of magic. It’s an extension to the lovely SQLite database that allows you to work with spatial data – instead of selecting data within tables, you can select within polygons, or intersections with lines, or within a distance of a point.

I’m going to try to avoid having too many maps here, as maps are a snapshot of a particular view of a GIS at a certain time. Maps I can make; GIS is what I’m trying to learn.

So, download the data and load up SpatiaLite GUI. Here I’ve created a new database file. addresses.sqlite. I’m all ready to load the shapefile.

Shapefiles are messy things, and are definitely glaikit. Firstly, they’re a misnomer; a shapefile is really a bunch of files which need to be kept together. They’re also a really old format; the main information store is actually a dBaseIII database. They also have rather dodgy ways of handling projection metadata. For all their shortcomings, no-one’s come up with anything better that people actually use.

Projection information is important, because the world is inconveniently unflat. If you think of a projected X-Y coordinate system as a graph paper Post-It note stuck to a globe, the grid squares depend on where you’ve decided to stick the note. Also, really only the tiny flat part that’s sticking to the globe closely approximates to real-world coordinates.

Thankfully, the EPSG had a handle on all this projection information (and, likely, Post-It notes). Rather than using proprietary metadata files, they have a catalogue of numbers that exactly identify map projections. SpatiaLite uses these Spatial Reference System Identifiers (SRIDs) to keep different projections lined up.

Toronto says its address data is in ‘MTM 3 Degree Zone 10, NAD27’. That’s not a SRID. You can list all the SRIDs that SpatiaLite knows with:

select * from spatial_ref_sys

which returns over 3500 results.

As we know there’s an MTM (Modified Transverse Mercator) and a 27 in the title, we can narrow things down:

select srid,ref_sys_name from spatial_ref_sys where ref_sys_name like '%MTM%' and ref_sys_name like '%27%'

The results are a bit more manageable:

srid

ref_sys_name

2017

NAD27(76) / MTM zone 8

2018

NAD27(76) / MTM zone 9

2019

NAD27(76) / MTM zone 10

2020

NAD27(76) / MTM zone 11

2021

NAD27(76) / MTM zone 12

2022

NAD27(76) / MTM zone 13

2023

NAD27(76) / MTM zone 14

2024

NAD27(76) / MTM zone 15

2025

NAD27(76) / MTM zone 16

2026

NAD27(76) / MTM zone 17

32081

NAD27 / MTM zone 1

32082

NAD27 / MTM zone 2

32083

NAD27 / MTM zone 3

32084

NAD27 / MTM zone 4

32085

NAD27 / MTM zone 5

32086

NAD27 / MTM zone 6

So it looks like 2019 is our SRID. That last link goes to spatialreference.org, who maintain a handy guide to projections and SRIDs. (Incidentally, Open Toronto seems to use two different projections for its data – the other is ‘UTM 6 Degree Zone 17N NAD27’ with a SRID of 26717.)

So let’s load it:

This might take a while, as there are over 500,000 points in this data set.

If you want to use this data along with more complex geographic queries, add a Spatial Index by right-clicking on the Geometry table and ‘Build Spatial Index’. This will take a while again, and make the database file quite huge (128MB on my machine).

Update: there’s a much quicker way of doing this without messing with invproj in this comment.

Now we’re ready to geocode. I was at the Toronto Reference Library today, which is at 789 Yonge Street. Let’s find that location:

Incidentally, I didn’t just magic up that weird invproj line. Most spatial databases use proj to convert between projections, and carry an extra column with the command line parameters. For our SRID of 2019, we can call it up with this:

An absolute tonne of data, in vector and raster formats. Services I’ve used are CanVec (vector data covering almost every feature) and Toporama (raster topographic maps; it has an associated Toporama Web Map Service).